Síntese de superfícies seletivas de frequência multicamadas via otimização bioinspirada

Abstract

The analysis of electromagnetic devices via computer software usually demands high computational cost and high processing time. In certain situations, to meet certain design objectives, finding the optimal structural parameters can take days or even weeks when done by trial and error when seeking accurate answers in highly complex structures. In this scenario, bioinspired computation (BIC) tools are strong allies in saving time, computational cost and, consequently, money. To enhance the power and efficiency of these tools, hybrid methods have been developed in which neural networks work in conjunction with optimization algorithms to obtain even more satisfactory and accurate results. In this context, this work presents the use of two multiobjective bioinspired hybrid optimization models for the design and synthesis of multilayer frequency selective surfaces (FSS). Initially, an electromagnetic investigation of the unit cell of the patch-like structures that will compose the multilayer FSS is made, which are a triangular loop and a solid diamond printed on fiberglass substrate (FR-4). The computer simulations were performed with the aid of CSTR○ Micro Wave Studio software, whose finite integrals (FIT) numerical technique is used. Three filters with distinctive characteristics that cover the C, X and Ku bands are designed. The synthesis process consists of tuning the objectives of the structures inserted in the cost function of the optimization algorithms. The modeling of the structures is performed by a general regression neural network (GRNN) and the optimization process is performed by the algorithms. The computational simulations for calculating the electromagnetic (EM) data of the multilayer FSS were performed using the CSTR○ software. The optimized values returned by the hybrid models were also simulated using Ansoft 𝐷𝑒𝑠𝑖𝑔𝑛𝑒𝑟𝑇𝑀 HFSS software to evaluate the previously obtained results. Good agreement between the simulated results was observed, showing a reduction in the processing time of the structures, besides showing that the GRNN-AG Multi model stood out in relation to the GRNN-MOCS, presenting errors in relation to the design objectives for the simulations. in CSTR○ of 0.44%, 0.254% and 0.387% for filters 1, 2 and 3, respectively, which is the most efficient hybrid model for multi-layer FSS optimization.